This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.
This paper proposes an algorithm to infer the maneuver intention of an obstacle ship and to check its compliance with the maritime traffic rules to avoid ship collision and ensure maritime traffic safety. A probabilistic graphical model is constructed to represent the relationship between motion observations of the obstacle ship and its hidden maneuver intention to comply with the traffic rules. The probabilistic belief of the ship's intention is modeled and quantified using probabilistic tools such as dynamic Bayesian networks. Three different intent inference models are formulated considering the different levels of observation configurations, and their calculation procedures are described. To demonstrate the feasibility of the proposed intent inference algorithm, Monte-Carlo simulations were conducted and the results are presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.